An introduction to the openseries Python package

First we set it all up

from openseries.frame import OpenFrame
from openseries.series import OpenTimeSeries

from plotly.offline import init_notebook_mode
from plotly.offline import iplot

init_notebook_mode()

Here we fetch NAV (price per unit) for some of the Captor mutual funds

basket = OpenFrame(
    [
        OpenTimeSeries.from_open_nav(isin="SE0009807308"),
        OpenTimeSeries.from_open_nav(isin="SE0011337195"),
        OpenTimeSeries.from_open_nav(isin="SE0011670843"),
    ], weights=[
        0.25, 
        0.25, 
        0.50])

basket = basket.trunc_frame().to_cumret()

We then make a portfolio of the funds based on the provided weights

portfolio = OpenTimeSeries.from_df(basket.make_portfolio("Portfolio"))
basket = basket.add_timeseries(portfolio)

And create a Plotly plot of all the data

figure, _ = basket.plot_series(tick_fmt=".1%", auto_open=False, output_type="div")

We display the plot in the notebook

figure = figure.update_layout(legend=dict(yanchor="bottom", y=-0.22, xanchor="right", x=0.98, orientation="h"))
iplot(figure, link_text="")

Here we calculate and display a Pandas Dataframe with some analyses

df = basket.all_properties(properties=[
        "arithmetic_ret",
        "vol",
        "ret_vol_ratio",
        "sortino_ratio",
        "worst_month",
        "cvar_down",
        "first_indices",
        "last_indices",
    ]
)

df.columns = df.columns.droplevel(level=1)

formats = [
        "{:.2%}",
        "{:.2%}",
        "{:.2f}",
        "{:.2f}",
        "{:.2%}",
        "{:.2%}",
        "{:%Y-%m-%d}",
        "{:%Y-%m-%d}",
    ]

for item, f in zip(df.index, formats):
    df.loc[item] = df.loc[item].apply(
        lambda x: x if isinstance(x, str) else f.format(x)
    )

df

	Captor Iris Bond	Captor Dahlia Green Bond	Captor Scilla Global Equity	Portfolio
Arithmetic return	-4.12%	-2.76%	6.37%	1.47%
Volatility	6.52%	3.40%	12.74%	7.12%
Return vol ratio	-0.63	-0.81	0.50	0.21
Sortino ratio	-0.88	-1.08	0.66	0.27
Worst month	-6.49%	-4.83%	-12.25%	-7.54%
CVaR 95.0%	-0.99%	-0.58%	-2.00%	-1.13%
first indices	2019-02-12	2019-02-12	2019-02-12	2019-02-12
last indices	2022-11-17	2022-11-17	2022-11-17	2022-11-17

Thank you for listening